Bottom Line:
This increases the accuracy and reduces the computational expense compared with exhaustive search.This paper introduces a novel displacement tracking algorithm, with a search strategy guided by a data quality indicator.Comparisons with existing methods show that the proposed algorithm is more robust when the displacement distribution is challenging.

ABSTRACTDisplacement estimation is a key step in the evaluation of tissue elasticity by quasistatic strain imaging. An efficient approach may incorporate a tracking strategy whereby each estimate is initially obtained from its neighbours' displacements and then refined through a localized search. This increases the accuracy and reduces the computational expense compared with exhaustive search. However, simple tracking strategies fail when the target displacement map exhibits complex structure. For example, there may be discontinuities and regions of indeterminate displacement caused by decorrelation between the pre- and post-deformation radio frequency (RF) echo signals. This paper introduces a novel displacement tracking algorithm, with a search strategy guided by a data quality indicator. Comparisons with existing methods show that the proposed algorithm is more robust when the displacement distribution is challenging.

fig8: (a) B-mode image of the simulated data. The Ω-shaped region contains only noise. Outside the Ω-shaped region, white noise is superimposed on the RF signal, with less noise at the centre than at the axial extremities. Displacement obtained by (b) the single-seed algorithm and (c) the drop-out correction method.

Mentions:
In the second Field II simulation (Fig. 8a), the noisy arc was extended sideways to partition the well-correlated data into two disjoint regions. Such a situation is not uncommon when scanning in vivo, as we shall see in Section 3.3. To succeed, a tracking algorithm will need to recover the displacements in the two regions separately. The single-seed, quality-guided algorithm finds the correct displacement distribution above the arc (where the seed was planted) but is unable to propagate good displacement estimates across the arc and into the lower region – see Fig. 8b. The drop-out correction method chances upon a good solution in this case (Fig. 8c), since one of the spurious displacement estimates at the bottom edge of the arc just happens to be within a wavelength of the correct value. This good estimate is then propagated downwards and sideways in the first pass, and most of the remaining poor estimates are corrected in the second pass. However, erroneous displacement estimates remain at the bottom left corner of the frame.

fig8: (a) B-mode image of the simulated data. The Ω-shaped region contains only noise. Outside the Ω-shaped region, white noise is superimposed on the RF signal, with less noise at the centre than at the axial extremities. Displacement obtained by (b) the single-seed algorithm and (c) the drop-out correction method.

Mentions:
In the second Field II simulation (Fig. 8a), the noisy arc was extended sideways to partition the well-correlated data into two disjoint regions. Such a situation is not uncommon when scanning in vivo, as we shall see in Section 3.3. To succeed, a tracking algorithm will need to recover the displacements in the two regions separately. The single-seed, quality-guided algorithm finds the correct displacement distribution above the arc (where the seed was planted) but is unable to propagate good displacement estimates across the arc and into the lower region – see Fig. 8b. The drop-out correction method chances upon a good solution in this case (Fig. 8c), since one of the spurious displacement estimates at the bottom edge of the arc just happens to be within a wavelength of the correct value. This good estimate is then propagated downwards and sideways in the first pass, and most of the remaining poor estimates are corrected in the second pass. However, erroneous displacement estimates remain at the bottom left corner of the frame.

Bottom Line:
This increases the accuracy and reduces the computational expense compared with exhaustive search.This paper introduces a novel displacement tracking algorithm, with a search strategy guided by a data quality indicator.Comparisons with existing methods show that the proposed algorithm is more robust when the displacement distribution is challenging.

ABSTRACTDisplacement estimation is a key step in the evaluation of tissue elasticity by quasistatic strain imaging. An efficient approach may incorporate a tracking strategy whereby each estimate is initially obtained from its neighbours' displacements and then refined through a localized search. This increases the accuracy and reduces the computational expense compared with exhaustive search. However, simple tracking strategies fail when the target displacement map exhibits complex structure. For example, there may be discontinuities and regions of indeterminate displacement caused by decorrelation between the pre- and post-deformation radio frequency (RF) echo signals. This paper introduces a novel displacement tracking algorithm, with a search strategy guided by a data quality indicator. Comparisons with existing methods show that the proposed algorithm is more robust when the displacement distribution is challenging.